Text Simplification with Entailment Scoring to Remove Hallucinations
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Solution Overview
Problem
Current text simplification methods often generate hallucinations, especially when applied to domain-specific texts lacking suitable training data, leading to inconsistent or redundant information, which can be misleading in critical contexts like legal and medical documents.
Innovation Solution
A text simplification apparatus that uses a neural network to compute an entailment score for each sentence of the simplified text, distinguishing between original and additional information, and a pruning component to generate a modified text that excludes hallucinations, ensuring the output remains faithful to the original text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text simplification methods are used to convert complex texts into simplified texts, then readability and ease of understanding are improved, but hallucinations and inconsistent information are introduced
Solution Approach 1:
The system implements feedback by computing entailment scores that measure whether simplified text faithfully represents the original complex text. The neural network evaluates each simplified sentence against the source text, providing feedback on information consistency. This feedback mechanism allows the system to identify and correct hallucinations while maintaining readability improvements.
Solution Approach 2:
The patent introduces an intermediary entailment scoring mechanism between the text simplification process and the final output. The neural network acts as a mediator that evaluates the relationship between simplified and original texts, computing scores that indicate whether information has been preserved accurately. This intermediary layer prevents direct transmission of hallucinated content while allowing genuine simplifications to pass through.
2Productivity
If neural networks are used to generate simplified text, then text processing efficiency is improved, but hallucinations are generated
Solution Approach 1:
The system performs preliminary action by computing entailment scores for each simplified sentence before finalizing the output. The neural network proactively evaluates information consistency during the generation process, identifying potential hallucinations early. This preliminary verification step allows the system to maintain high processing efficiency while preventing inconsistent information from reaching the final output.
Solution Approach 2:
The entailment scoring mechanism provides continuous feedback during text generation, allowing the system to monitor and adjust for hallucinations in real-time. The feedback loop enables the neural network to maintain productivity while ensuring information consistency by identifying and correcting issues before they propagate through the simplified text.
3Adaptability or versatility
If complex domain-specific texts are simplified without domain-specific training data, then adaptability is improved, but hallucinations increase
Solution Approach 1:
The patent implements universality by designing an entailment scoring system that can evaluate text consistency across multiple domains without requiring domain-specific training data. The neural network performs the dual function of both simplifying text and verifying information consistency, making the system adaptable to various domains while maintaining reliability through its domain-agnostic entailment evaluation mechanism.
Data Source
AI summary
Systems and methods for text simplification are described. Embodiments of the present disclosure identify a simplified text that includes original information from a complex text and additional information that is not in the complex text. Embodiments then compute an entailment score for each sentence of the simplified text using a neural network, wherein the entailment score indicates whether the sentence of the simplified text includes information from a sentence of the complex text corresponding to the sentence of the simplified text. Then, embodiments generate a modified text based on the entailment score, the simplified text, and the complex text, wherein the modified text includes the original information and excludes the additional information. Embodiments may then present the modified text to a user via a user interface.